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#!/usr/bin/env python3
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
import os
import json
from tqdm import tqdm
import pandas as pd
import multiprocessing
import time
import torch
def job(utt_list, parquet_file, utt2parquet_file, spk2parquet_file):
start_time = time.time()
data_list = []
for utt in tqdm(utt_list):
data = open(utt2wav[utt], 'rb').read()
data_list.append(data)
wav_list = [utt2wav[utt] for utt in utt_list]
text_list = [utt2text[utt] for utt in utt_list]
spk_list = [utt2spk[utt] for utt in utt_list]
uttembedding_list = [utt2embedding[utt] for utt in utt_list]
spkembedding_list = [spk2embedding[utt2spk[utt]] for utt in utt_list]
speech_token_list = [utt2speech_token[utt] for utt in utt_list]
# 保存到parquet,utt2parquet_file,spk2parquet_file
df = pd.DataFrame()
df['utt'] = utt_list
df['wav'] = wav_list
df['audio_data'] = data_list
df['text'] = text_list
df['spk'] = spk_list
df['utt_embedding'] = uttembedding_list
df['spk_embedding'] = spkembedding_list
df['speech_token'] = speech_token_list
df.to_parquet(parquet_file)
with open(utt2parquet_file, 'w') as f:
json.dump({k: parquet_file for k in utt_list}, f, ensure_ascii=False, indent=2)
with open(spk2parquet_file, 'w') as f:
json.dump({k: parquet_file for k in list(set(spk_list))}, f, ensure_ascii=False, indent=2)
logging.info('spend time {}'.format(time.time() - start_time))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--num_utts_per_parquet',
type=int,
default=1000,
help='num utts per parquet')
parser.add_argument('--num_processes',
type=int,
default=1,
help='num processes for make parquets')
parser.add_argument('--src_dir',
type=str)
parser.add_argument('--des_dir',
type=str)
args = parser.parse_args()
utt2wav, utt2text, utt2spk = {}, {}, {}
with open('{}/wav.scp'.format(args.src_dir)) as f:
for l in f:
l = l.replace('\n', '').split()
utt2wav[l[0]] = l[1]
with open('{}/text'.format(args.src_dir)) as f:
for l in f:
l = l.replace('\n', '').split()
utt2text[l[0]] = ' '.join(l[1:])
with open('{}/utt2spk'.format(args.src_dir)) as f:
for l in f:
l = l.replace('\n', '').split()
utt2spk[l[0]] = l[1]
utt2embedding = torch.load('{}/utt2embedding.pt'.format(args.src_dir))
spk2embedding = torch.load('{}/spk2embedding.pt'.format(args.src_dir))
utt2speech_token = torch.load('{}/utt2speech_token.pt'.format(args.src_dir))
utts = list(utt2wav.keys())
# Using process pool to speedup
pool = multiprocessing.Pool(processes=args.num_processes)
parquet_list, utt2parquet_list, spk2parquet_list = [], [], []
for i, j in enumerate(range(0, len(utts), args.num_utts_per_parquet)):
parquet_file = os.path.join(args.des_dir, 'parquet_{:09d}.tar'.format(i))
utt2parquet_file = os.path.join(args.des_dir, 'utt2parquet_{:09d}.json'.format(i))
spk2parquet_file = os.path.join(args.des_dir, 'spk2parquet_{:09d}.json'.format(i))
parquet_list.append(parquet_file)
utt2parquet_list.append(utt2parquet_file)
spk2parquet_list.append(spk2parquet_file)
pool.apply_async(job, (utts[j: j + args.num_utts_per_parquet], parquet_file, utt2parquet_file, spk2parquet_file))
pool.close()
pool.join()
with open('{}/data.list'.format(args.des_dir), 'w', encoding='utf8') as f1, \
open('{}/utt2data.list'.format(args.des_dir), 'w', encoding='utf8') as f2, \
open('{}/spk2data.list'.format(args.des_dir), 'w', encoding='utf8') as f3:
for name in parquet_list:
f1.write(name + '\n')
for name in utt2parquet_list:
f2.write(name + '\n')
for name in spk2parquet_list:
f3.write(name + '\n')
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